Estimating mutation rates under heterogeneous stress responses.

Area of Science:

• Microbiology
• Evolutionary Biology
• Genetics

Background:

• Environmental stressors, including antibiotics, trigger bacterial stress responses.
• Some stress responses elevate mutation rates, potentially accelerating bacterial resistance evolution.
• Existing methods for estimating mutation rates under stress do not account for heterogeneous stress response expression in bacterial populations.

Purpose of the Study:

• To develop a population dynamic model addressing heterogeneous stress responses and their impact on mutation and division rates.
• To derive a method for estimating mutation-rate increases specifically linked to stress response expression.
• To differentiate between mutation rate heterogeneity and mutant fitness costs in fluctuation assay data.

Main Methods:

• Developed a population dynamic model incorporating heterogeneous stress responses (on/off subpopulations).
• Modeled impacts of stress responses on both mutation and cell division rates, inspired by E. coli's SOS response.
• Derived mutant count distribution for fluctuation assays and implemented maximum likelihood estimation.

Main Results:

• The new inference method accurately estimates mutation-rate increases under stress, especially when the increase is substantial and division rate is reduced.
• The study highlights that both heterogeneous stress responses and mutant fitness costs can yield similar fluctuation assay patterns, necessitating further investigation.
• Current methods accurately estimate the population mean mutation rate increase, but the novel method can infer distinct stress-induced mutation rates.

Conclusions:

• A novel population dynamic model and inference method accurately quantify stress-induced mutation rate increases in bacteria, even with heterogeneous responses.
• Distinguishing between heterogeneous stress responses and mutant fitness costs is crucial for understanding bacterial evolution.
• The developed method offers a way to parameterize evolutionary models with distinct stress-induced mutation rates.